Using functions from fuzzy classes of k-valued logic for decision making based on the results of rating evaluation

• Autorzy: Poleshchuk O. N.

Identyfikatory

DOI

10.14313/JAMRIS_4-2017/38

• Języki publikacji: EN

Abstrakty

EN

IIn this paper we present an approach based on logic functions with fuzzy conditions for constructing a decision support system for rating evaluation of objects. This approach provides an effective and efficient way for separating rating marks into clusters, associated with a control effect directed at a successful functionality of objects in future.

Słowa kluczowe

EN

rating marks; linguistic statements; fuzzy logic functions; decision support

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2017
• Tom: Vol. 11, No. 4
• Strony: 73--79
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Poleshchuk O. N.

• Department of Mathematics, Bauman Moscow State Technical University, str. Baumanskaya 2-ya, 5, Moscow 105005, Russian Federation

Bibliografia

• [1] Poleshchuk O., “The determination of students’ fuzzy rating points and qualification levels”, International Journal of Industrial and Systems Engineering, 2011, vol. 9, no. 1, 13–20.
• [2] Poleshchuk О., Komarov Е. “The determination of rating points of objects with qualitative characteristics and their usage in decision making problems”, International Journal of Computational and Mathematical Sciences, 2009, vol. 3, no. 7, 360 – 364.
• [3] Poleshchuk О., Komarov Е., “The determination of rating points of objects and groups of objects with qualitative characteristics.” In: Annual Conference of the North American Fuzzy Information Processing Society, 2009, p. 5156416.
• [4] Ryjov A., “Fuzzy data bases: description of objects and retrieval of information.” In: Proceeding of the First European Congress in Intelligent Technologies, 1993, vol. 3, 1557– 1562.
• [5] Rogozhin S., Ryjov A., “Fuzzy classes in k-valued logic.” In: V National conference “Neurocomputers and applications”, 1999, 460–463.
• [6] Darwish A., Poleshchuk O., “New models for monitoring and clustering of the state of plant species based on sematic spaces”, Journal of Intelligent and Fuzzy Systems, 2014, vol. 26, no. 3, 1089–1094.
• [7] Zadeh L.A., “The Concept of a linguistic variable and its application to approximate reasoning”, Part 1, 2 and 3, Information Sciences, 1975, vol. 8, 199–249, 301-357, 1976,vol. 9, 43–80.
• [8] Ryjov A., “The Concept of a Full Orthogonal Semantic Scope and the Measuring of Semantic Uncertainty.” In: Fifth International Conference Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1994, 33–34.
• [9] Poleshchuk O., Komarov E., “Expert Fuzzy Information Processing”, Studies in Fuzziness and Soft Computing, 2011, 1–239.
• [10] D. Dubois, H. Prade, “Fuzzy real algebra: some results,” Fuzzy Sets and Systems, 1979, vol. 2, no.4, 327–348.
• [11] Yager R., Filev D.P., “On the issue of defuzzification and selection based on a fuzzy set”, Fuzzy Sets and Systems, 1993, vol. 55, no. 3, 255– 272.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).